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There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

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The most common flow assay is undoubtedly immunophenotyping, in which fluorescently tagged antibodies are used to bind to cellular proteins. This allows you to determine the types of cells present. As long as there is a fluorescent reporter available, it is possible to measure biological processes using flow cytometry – especially in a phenotypically defined manner. Probably the most common of these assays is the calcium flux assay. And that is just the tip of the iceberg. In addition to calcium, it is possible to measure magnesium and zinc concentrations, reactive oxygen species, and even membrane potential using flow. Today, we’ll cover 4 assays that use a fluorescent reporter to measure their target, allowing researchers to challenge the cells and measure their response in real time.

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What is the single-most important feature of a flow cytometry experiment? Arguably, it’s the stained cells that gather data about biological processes of interest. However, a flow cytometer can measure cell-like particles as well as cells, which opens the realm of cytometry to the use of microspheres. Most researchers are familiar with the 4-Cs that beads can be used for: Control, Calibration, Compensation, and Counting. Beyond the 4-Cs, many are familiar with the multiplex bead assays for measuring analytes. Today, we will take a look beyond these well-known uses and discover the myriad applications of the “Mighty Microspheres.”

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Compensation is necessary due to the physics of fluorescence. Basically, compensation is the mathematical process of correcting spectral spillover from a fluorochrome into a secondary detector so that it is possible to identify single positive events in the context of a multidimensional panel. Good compensation requires that your controls tightly adhere to three rules. If the controls don’t meet this criteria, it will lead to faulty compensation resulting in false conclusions and poorly reproducible data. Even among flow cytometry veterans, a strong foundation is occasionally in need of a tune-up. And in a topic as complex as flow cytometry, it’s important that we review the fundamentals on a regular basis. In fact, it is the longtime cytometry expert who must check themselves for any sort of faith in older compensation practices. Science is ever a work in progress, and traditional methods are not always the right methods.

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In the flow cytometry community, SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a favored algorithm for dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. Given the growing popularity of this kind of algorithm for dealing with complex datasets, we decided to test the SPADE algorithm in 5 software packages, including Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE. Which was the fastest?

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Because mass cytometry allows users to characterize masses so effectively, data can be normalized much more efficiently than what traditional fluorescent flow will permit. If there is no working CyTof at your institution, you can still partner with CyTof-friendly research institutions that have the technology on hand. And because the samples are fixed, you can ship them overnight. This way, they will be analyzed for you. Today’s article will summarize the functionality of mass cytometry technology. This tech has been commercialized largely by Fluidigm in the CyTof systems. There are 5 key points to cover, or takeaways, that cytometrists should keep in mind as they perform their research. The 5 points include how mass cytometry works, panel design, proper sample preparation, data analysis, and imaging mass cytometry.

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Reproducibility is key to the scientific method. After the results of a study are published, the community validates the findings and extends them. If the findings are not reproducible, the second step is impossible. With performable experiments increasing in complexity, and the concurrent increase in the cost of equipment and reagents to perform these experiments, it is important to find the best way to maximize the money spent on advancing research. In flow cytometry, there are many places where improvements can be made to increase the consistency and reproducibility of an experiment. The most obvious place is in the instrument, but today’s focus is on the reagents we use to identify cells of interest: Antibodies and fluorochromes.

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All flow cytometer instruments have a certain 3 components, and the way they are put together will dictate the performance of the system. As a user, you’ll be interacting heavily with these components, so you need to know both what they are and how they work. There are fluidics, optics, and electronics. The fluidics allow you to interact at the right flow rate so that your data keep a tight CV. Then you can run the same flow rate for all your samples, and you won’t have different CVs for different samples. There are also different optics you can use, like PMTs, APDs, and PDs. It’s important to remember the bandpass filters because they indicate the detector on which your signal will be measured. And with a newer generation of instruments, you can actually change out bandpass filters and design the flow cytometer to your specifications – just make sure you cite the specific bandpass filter that you use. Finally, there are electronics, which process the photon into an electronic signal that is ultimately digitized and stored in a file known as the “FCS file.” An analysis can be performed on this file at a later time.

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Did you know that tissues can be measured by flow cytometry? Flow cytometry is the measurement of cellular processes at the whole-cell level. This definition is useful because it includes not only flow cytometry, but any technique that measures at the level of the whole cell. Microscopy, for instance, is a great example of cytometry. But, what can be measured by flow cytometry? For one, tissues with lots of cells. When flow cytometry is practiced, the cells are broken up. Therefore, any cellular interactions within the sample are also broken up. This includes tissues, cell-to-cell contacts in tissues, and virtually any information about the microenvironment. As we continue to discover, the microenvironment can play a dramatic role in cell development, influencing how cells grow and change. This article will discuss how to analyze tissues and microenvironments by flow cytometry.

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I am still convinced that my first cell sorter was possessed. The number of issues that I had with the system remains hard for me to believe, even after all these years.
It had been purchased, in part, from one vendor because the sales rep for a competitor was nowhere to be found. At that time, I admit I wasn’t overly diligent in my research process. Since then, I’ve pinpointed some critical questions that need to be answered before purchasing a new instrument.
At the end of the process, a shiny new instrument will arrive at your facility. Make sure you find time to do a shakedown and validate the system. This is the time to get to know it better, identify quirks and potential issues, and develop training and QC programs. Once your shakedown is complete, you can start adding users and encouraging feedback on the system.

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It has been said that “a picture is worth a thousand words.” We are visual creatures, and we seek to capture and describe the world around us. Some of the earliest evidence for this comes from very old cave paintings found around the world, like this painting of a horse found in the caves in Lascaux, France.

With the development of reliable microscopes, such as those developed by the dutch draper Antonie van Leeuwenhoek, we were able to see what was previously invisible, probing the unseen and learning in great detail how organisms worked.

Over time, the field of cytometry (the analysis of biological processes at the whole-cell level) has expanded in many different directions. Flow cytometry can be thought of as a microscope with very poor resolution. The power of flow cytometry lies in its ability to analyze thousands of cells through many dimensions, providing an amazingly detailed understanding of the cell. However, due to the resolution, it is not possible to tell where these signals are located.

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Quality control is the hallmark of improving reproducibility. QC programs are designed to help determine when the process in question goes off the expected path. Depending on the deviation from the established acceptance criteria will dictate the level of intervention that needs to occur. This can be as easy as cleaning the instrument and rerunning the QC, or as extreme as removing the data from the final analysis. Since there is documentation as to the deviations, this provides the rationale for excluding data.

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FlowJo is a powerful tool for performing and analyzing flow cytometry experiments, if you know how to use it to the fullest. This includes understanding embedding and using keywords, the FlowJo compensation wizard, spillover spreading matrix, FlowJo and R, and creating tables in FlowJo. Extending your use of FJ using these hacks will help organize your data, improve analysis and make your exported data easier to understand and explain to others. Take a few moments and explore all you can do with FJ beyond just gating populations.

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To get the best flow cytometry data you need to be thinking about all the steps in your experiment to ensure that you have high-quality data to analyze. To improve the quality of your analysis make sure you’re adding keywords at the beginning of your experimental setup, develop a quality control program, trust but verify any software wizards, use proper controls, and make sure you extract the correct data.

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Reproducibility is a state of mind. It’s not one simple thing that you do that will make all your data more reproducible, it a shift in the way one thinks about and perform experiments. With the emphasis on rigor and reproducibility in science, it’s very important that researchers start putting into place everything they can do to help improve the quality and reproducibility of there data. Learn 3 action steps that can be taken to enhance experimental reproducibility.

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Cell death is a natural part of the lifecycle of a cell. In cases of development, it is critical for the shaping of fingers during human development. The processes of ordered cell death, or Apoptosis, are so important that in 2002, Sidney Brenner, Robert Horvitz, and John Sulston received the Nobel Prize in Medicine for their work on understanding this process. There are many different ways to measure cell death and flow cytometry is an ideal tool for this technique. Whether you are just assessing the viability of your cells or you are interested in the exact stage of cell death your sample is in, there are a variety of ways that you can measure cell death. Learn 3 ways you can use flow cytometry to measure cell death.

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Reproducibility is a state of mind. It’s not one simple thing that you do that will make all your data more reproducible, it a shift in the way one thinks about and perform experiments. With the emphasis on rigor and reproducibility in science, it’s very important that researchers start putting into place everything they can do to help improve the quality and reproducibility of there data. Learn 3 action steps that can be taken to enhance experimental reproducibility.

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There are so many downstream applications of cell sorting, but if you don’t take the time to do you cell sort the right way your downstream experiments won’t work. In order to have the most success with your cell sort be sure you consider these 3 things, size dictates almost everything you are going to do, sample preparation is key, and think about what type of tube you are collecting your cells in. If you account for those 3 things you will set yourself up for a successful cell sort and successful downstream applications.

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Controls are an incredibly important part of your flow cytometry experiments. If not done correctly, poor controls will waste time and money. But with proper care, high-quality controls will result in high-quality data. Just be sure to ask yourself these key questions, should you be using isotype controls, do you have a quality control procedure in place, and are you following the 3 cardinal rules of compensation.

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A lot of the troubleshooting is focused on fluidics issues. If you sit down and think about your workflow, and how you might want to add a couple of little tweaks here and there which will ultimately help you improve the quality of your data as well as aid you in identifying issues before they become problems your troubleshooting will be much smoother. Consider these three things, what do you before you start collecting data, ensure you have appropriate plots of time vs fluorescence for each of the lasers your using and apply appropriate gating procedures.

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Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner.
When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the cells? The requirements of the downstream assay will also dictate the purity needed.

The cell type being used will, in part, dictate the speed of sorting. Smaller cells can be sorted faster because a smaller nozzle can be used.

When you start a cell sort it’s important that you are aware of the downstream analysis and assays that you want to run. This will determine how you perform the sort and how you determine if your sort was successful or not.
Successful cell sorting involves balancing recovery, yield and speed. What do these three terms mean and what influences each of these factors?

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Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner.
When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the cells? The requirements of the downstream assay will also dictate the purity needed.

The cell type being used will, in part, dictate the speed of sorting. Smaller cells can be sorted faster because a smaller nozzle can be used.

When you start a cell sort it’s important that you are aware of the downstream analysis and assays that you want to run. This will determine how you perform the sort and how you determine if your sort was successful or not.
Successful cell sorting involves balancing recovery, yield and speed. What do these three terms mean and what influences each of these factors?

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As a researcher, you want to achieve the best cell sorting possible. So, how can you achieve that? There are clear strategies you can use to achieve great cell sorting results, including finding your ideal sample concentration, using magnetic sorting to enrich your population, suspending cells in the right buffer to avoid cell clumps, changing your instrument settings when sorting small cells, and optimizing your sample preparation and instrument when sorting large cells.

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Cell cycle seems like such a straightforward assay. At its heart, it is a one-color assay and should be a simple protocol to follow. However, as discussed before, fixation and dye concentrations are critical. Once those are optimized, it becomes important to run the cells low and slow in order to get the best quality histograms for analysis — the topic of another blog. Adding the critical CEN and TEN controls will help standardize the assay, and ensure consistency and reproducibility between runs while helping identify non-standard (aneuploid, polyploid) populations from normal ploidy. Trying to isolate and focus on specific components of the cell cycle can be done by addition of specific antibodies or using thymidine analogs. In the end, cell cycle analysis is a simple assay that has a great deal of potential. With work and optimization, a great deal of information about the life of a cell can be extracted.

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Cell cycle analysis appears to be deceptively easy in concept, but details are absolutely critical. It is not possible to hide the data if there is poor sample preparation, incorrect dye ratios, too much (or too little) staining time, etc. Forgetting RNAse when using PI will doom your data to failure. Take these basics into account as you move into performing this simple, yet amazingly informative assay.

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The topic of compensation is a critical one for the cytometrist to understand. It requires adherence to some specific rules, an understanding of how the instrument works, and how fluorescence occurs. Poor or incorrect compensation can easily lead to incorrect conclusions, and decreases the reliability and robustness of the data generated.
It is critical to question the wisdom of the “Protocol’s Book” and understand that the “truths” in this book are not always correct anymore. The new user doesn’t necessarily know any differently, and for this reason there are suboptimal practices that permeate flow cytometry experiments to this day.

Understanding compensation, and being armed with the knowledge, allows the researcher to combat those fairytales that continue to make their rounds in science. It is time to put them to bed and move forward with a full understanding of the process.

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Understanding the 3 rules of compensation, and applying them to your everyday workflows, is an essential step in good, consistent, and reproducible flow cytometry data. Making sure the controls are bright, and treated the same way, is essential. Don’t bring unfixed controls when your samples are fixed, as the controls will not reflect the spectra from the fixed samples. Make sure not to rely on the “Universal Negative”, use a single sample to set background, and collect enough events to make sure an accurate measurement is made, as this will further improve the quality of your control and therefore the data.

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3 different theories on compensation are discussed. The first, non-pensaton, is not recommended, and only possible under a narrowly defined instrument. The second, manual compensation, is also not recommended for anything more than 2 fluorochromes. It is error prone and subject to the researcher’s judgement, unless statistics are invoked and then it becomes a tedious and difficult exercise in algebra. For polychromatic flow cytometry, best practices in flow cytometry is to use the automated compensation methodologies. This will ensure consistent and accurate compensation, if some rules are followed.

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Why is the speed of the algorithm so important? Why worry when you can just set up the analysis and go for lunch? If you’re like me, when I’m analyzing data, I like to stay in that mindset. Distractions, like a long break, can impact the train of thought about the analysis. Additionally, with long run-times, it is depressing to return to the data and see the calculation stopped prematurely because of an incorrect parameter or some other error.

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The flow cytometer is an integral component of any flow cytometry experiment, and special attention should be paid to ensuring that it is working correctly and consistently. As an end-user, the researcher should be able to sit down at a machine and know that it is performing the same way today as it was yesterday and last week. Equally important is that if any changes in instrument performance have occured, the end-user knows how they have been addressed and corrected, rather than letting them fester and potentially affect the results. Quality control measurements can include a variety of targets, such as PMT sensitivity, laser alignment, fluidic stability, background issues, and more.

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With the increased focus on reproducibility of scientific data, it is important to look at how data is interpreted. To assist in data interpretation, the scientific method requires that controls are built into the experimental workflow. These controls are essential to minimize the effects of variables in the experiment so that changes caused by the independent variable can be properly elucidated. Getting into the mindset to improve the reproducibility of flow cytometry experiments requires a hard look at the appropriate controls to use in each experiment.

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It is necessary to sort through hundreds of thousands or millions of cells to find the few events of interest. With such low event numbers, we move away from the comfortable domain of the Gaussian distribution and move into the realm of Poisson statistics. There are 3 points to consider to build confidence in the data that the events being counted are truly events of interest and not random events that just happen to fall into the gates of interest.

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Stem cells, circulating tumor cells, and minimal residual disease in cancer patients were all discovered through the power of rare event flow cytometry. When preparing for rare event analysis, sample preparation and data analysis must be taken into account at the beginning. How will we stain our cells? How will we analyze our cells? What controls will we use to help us identify our rare events? What statistical methods do we use to analyze our results? Here are 5 procedural limitations that impact the quality of rare event flow cytometry data and how to optimize your assay to get the best results possible.

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For those new to flow cytometry, compensation is confusing at best and terrifying at worst. Likewise, those who have been doing flow cytometry since the analog ages may be holding on to practices that, while suited to the analog instruments, should be left to the annals of history. As such, a lot of time is spent discussing compensation and the best practices for this critical process. There are 3 rules that guide proper compensation, and they’ve been written about extensively since they first appeared in the “Daily Dongle” in 2011. Here, we will review the classic rules and expand upon the tacit assumptions required to fulfill them.

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Congratulations, your grant has been funded! Next comes generating data and publishing papers. What was that hypothesis again? It must be in the grant somewhere, right? To avoid even the appearance of HARKing — Hypothesizing After The Results Are Known — it is important to start the statistical analysis process even before the first experiments are performed. This process consists of 5 steps: setting the null hypothesis, establishing a threshold, performing the experiments, performing the statistical tests, and communicating the results. Walk with us as we discuss these steps in an example workflow.

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With the added emphasis on reproducibility, it is critical to look at every step where experiments can be improved. No single step makes an experiment more reproducible, rather it is a process, making changes at each stage that leads to reproducibility. Antibodies comprise a critical component that needs to be reviewed. As Bradbury et al. in a commentary in Nature pointed out, the global spending on antibodies is about $1.6 billion a year, and it is estimated about half of that money is spent on “bad” antibodies. This does not include the additional costs of wasted time and effort by the researcher using these bad antibodies. Using tools to identify the best reagent to use, considering a switch to recombinant antibodies, and properly validating reagents for use in an assay, are 3 steps that will improve the reproducibility of your experiments.

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Dyes exist for the detection of everything from large nucleic acids to reactive oxygen species, and from lipid aggregates to small ions. Concentrations of physiologically important ions such as sodium, potassium, and calcium can be important indicators of health and disease. Calcium ions play an especially critical role in cellular signaling. As a signaling messenger, calcium is involved in everything from muscle contractions, to cell motility, to enzyme activity. Calcium experiments can be very informative, and with the advent of cheaper UV lasers, more and more researchers can use ratiometric measurements to evaluate the signaling processes in phenotypically defined populations.

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Flow cytometry is designed to measure physical and biochemical characteristics of cells and cell-like particles using fluorescence. Fundamentally, any single-particle suspension (within a defined size range) can pass through the flow cytometer. Beads, for better or worse, are a sine qua non for the flow cytometrist. From quality control,to standardization, to compensation, there is a bead for every job. They are important — critical, even — for flow cytometry.

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There is a lot of preparatory work that must be done before the first flow cytometry experiment can be attempted. Each step builds upon the previous one and extends where the assay is going. Be prepared for some trial and error in this process, and don’t expect perfect results the first time around. An educated user is a good user, and makes the SRL staff’s job that much easier. The partnership between investigator and SRL staff is a rewarding one, when both parties work together to achieve the ultimate goal of generating excellent data and sort results that help answer the biological question being tested.

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Here, we cover 5 lessons from the trenches of flow cytometry looking at important aspects of how best practices have changed over time, which practices need to be adopted, and which are outdated. Put those old, coffee-stained protocols away and take advantage of the best practices for digital instruments to write new and improved ones (coffee stains optional). Your data will thank you.

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You are probably familiar with the term, “doublet discrimination” or “doublet exclusion”, and have likely included this flow cytometry measurement into at least some (if not all) of your gating strategies. Even though you may utilize this important gating strategy, you may not have had the chance to delve deeper to explore exactly what doublets are and why it’s critical to exclude them. This article aims to give you insight on the what, why, and how of doublet discrimination.

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For those working in the signaling field, having the ability to take a sample and phenotypically identify it, while knowing what is happening inside the cell to the target molecules of choice opens up a host of new opportunities. These assays are amenable to high throughput setup, meaning that biologically relevant outcomes in pre-clinical drug discovery can be measured directly. All told, with a little forethought, some careful planning and validation, and our helpful tips, phosphoflow assays are within your reach.

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Flow cytometry is a numbers game. There are percentages of a population, fluorescence intensity measurements, sample averages, data normalization, and more. Many of these common calculations are useful, but surrounded by misconceptions. This primer will help you decide which calculation to use, when to use it, and how to interpret the results.

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Measuring the receptor occupancy of a given target showcases the power of flow cytometry. With the right reagents, best practices, and attention to detail, this assay can become a mainstay in your research toolkit. It extends quantitative flow cytometry to the next level, to determine a complete biological picture of how efficiently a given target is being bound. This also serves as the basis for even more fine-analysis when combined with assessment of downstream targets that the engagement of the receptor by the target antibody may affect. Phosphorylation, cell cycle arrest, and protein expression are all within reach, resulting in an even more complete picture of the process, that will ultimately give the medical community a fuller understanding of how these potential therapeutics work and when to use them. This is truly personalized medicine at its fullest potential.

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Some technological advances are incremental, while others are significant game-changing tools that offer the researcher the ability to significantly improve current assays while allowing for new and novel avenues of research to be performed. With speed, sensitivity, and capacity to spare, the ZE5 fits into the game-changing category. Reduced carryover, increased speed of acquisition, and a large number of parameters all open up new and novel assays while improving the quality and reproducibility of ongoing ones.

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The ultimate goal of any experiment is to analyze data and determine whether it supports or disproves a given hypothesis. To do that, scientists turn to statistics. If we wish to compare either a single group to a theoretical hypothesis, or two different groups, and these groups are normally distributed, the test of choice is the Student’s t-Test. To perform the t-Test, it is critical to start from the beginning of the experiment to establish several parameters, including the type of test, the null hypothesis, the assumptions about the data, the number of samples to be analyzed (Power of the experiment), and the threshold. The experiments are performed, and only then, after the primary analysis is completed, is statistical testing performed.

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One of the most common assays in flow cytometry is the surface labeling of cells with antibodies. Often termed “immunophenotyping”, it allows the researcher to identify, count, and isolate cells of interest in a mix of input cells. Every lab has their own favorite protocol to move from sample to cytometer, handed down from some hallowed, chemical-stained notebook, and followed as exactly as making a souffle. The real questions are, which of those steps are critical, and what other factors should be considered when staining cells? This article will focus on staining immune cells, but the principles apply in general, and specific issues for a specific sample type can be optimized in a similar way.

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Mass cytometry panels routinely include 30 or more markers, but traditional analysis methods like bivariate gating can’t adequately parse the resulting high-dimensional data. Spanning-tree progression analysis of density-normalized events (SPADE) is one of the most commonly used computational tools for visualizing and interpreting data sets from mass cytometry and multidimensional fluorescence flow cytometry experiments. There are two key parameters in SPADE that you can adjust in order get the best results possible: downsampling, and target number of nodes or k. Knowing how to properly set these values will enable you to enhance the quality of your analysis.

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Since the first laser was mounted to create the first flow cytometer, there has been a push for more – more lasers, more detectors, more colors. As a result, today’s researchers require a large number of lasers and detectors to ensure current panels can be run and new, expanded panels can be developed. This can be problematic because, in general, making one decision to improve a cell analyzer can limit the analyzer in other ways. It may seem like an impossible task, but the team of Bio-Rad and Propel Laboratories, collaborated to bring the ZE5™ Cell Analyzer to the market and, with thoughtful design, the Analyzer answers these challenges, resulting in a high-end, easy to use, automated flow cytometer.

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We hope this explanation sheds some light on scaling. Knowing how to properly display your data is a critical part of scientific communication. Remember to use linear scaling for most scatter parameters, or when you need to visualize small changes, and log scaling for most fluorescence parameters, or when you need to visualize a wide range of values. As always in flow cytometry, there are certainly exceptions, but armed with this knowledge, you should be able to make educated judgements about which scale types to use in various assays and to better interpret your data.

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There are several areas that researchers can focus on to improve the reproducibility of their flow cytometry experiments. From instrument quality control, through validation of reagents, to reporting out the findings, a little effort will go a long way to ensure that flow cytometry data is robust, reproducible, and accurately reported to the greater scientific community. Initiatives by ISAC have further offered additional levels of standards to support these initiatives, which were developed even before the Reproducibility Crisis came to a head in both scientific and popular literature.

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Until we have access to well-validated recombinant antibodies produced under tightly regulated conditions, researchers need to exercise good judgment regarding these critical biological reagents. These 4 steps will help ensure that your results are consistent and reproducible. This will both reassure your reviewers that your data is of high quality, and allow for researchers at other institutions to successfully replicate your results. In addition, identifying antibody duds early on will save you time and money in the long run. Don’t shirk the work of ensuring your antibodies are working correctly and targeting the right proteins.

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Considerations that must be made when choosing fluorochromes include the brightness of the dyes in question, the instrument configuration, and the staining protocol. Each of these factors will impact the quality of the data because of issues related to spectral spillover, staining, loss of signal because of tandem dye degradation, the ability to get an antibody/fluorochrome into a cell, and more. It takes time and effort to develop and optimize a panel. If one fluorochrome doesn’t work, consider why it may have failed and look for alternatives.

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It is critical to prepare for your statistical analysis at the beginning of the experimental design process. This will ensure the correct data is extracted, the proper test applied, and that sufficient replicates are obtained so that if an effect is to be found, it will be found. Here are five considerations to implement into your experimental design to ensure the best statistical methods so that your data stands up to review.

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Fluorescence compensation is not possible without proper controls, so it is critical to spend the time and effort to generate high-quality controls in the preparation of an experiment. For a compensation control to be considered “good” or “proper”, each compensation control must be as bright as or brighter than the experimental stain, autofluorescence should be the same for the positive and negative populations used for the compensation calculation in each channel, and the fluorophore used must be the exact fluorophore (i.e. same molecular structure) that is used in the experimental sample.

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Getting a clear signal with reduced noise is an essential component to good data. Adding a threshold when acquiring flow cytometry data is one way to do that. It reduces the number of events by setting a bar that a signal pulse must clear before it is counted as an event. Depending on the importance of the data, the downstream applications for the data (or sorted cells) will dictate how critical the threshold is. In combination with proper sample preparation, appropriate thresholding will reduce debris and ensure best outcome.

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The best way to take out the fear and agony of setting voltages is to use some optimization methods. The peak 2 method is a useful and robust method of identifying optimal PMT voltage ranges. Refining that to the voltage walk with the actual cells and fluorochromes of interest will further improve sensitivity, which is especially critical for rare cell populations or emergent antigens. This article describes how to set up, monitor, and maintain optimal voltage settings for your flow cytometry experiment.

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While controls are critical for minimizing the effects of variables in your flow cytometry experiments, choosing the right controls are essential. When your research is published, reviewers need to see that your variables have been analyzed properly. Evaluating strengths and weaknesses will give you information and back up arguments for the case for or against isotype controls. Here’s a review of what isotype controls are and if you need to use them.

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From photon, through conversion from light, into an electron, into an electronic signal pulse. The journey of a photon, through to its data representation via flow cytometry, involves the detection system and the electronics, and basically following a bouncing photon to its ultimate digitization. This article provides 3 key takeaways in the process from photons through to end data for analysis.

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Flow cytometry is a very powerful tool and can answer many questions if the experiments are properly designed. There is a learning curve that takes a bit of time, patience, and practice, but soon you may be finding excuses to perform flow cytometry experiments and we will be here to help you with best practices. Using this checklist will help you to design and perform consistent experiments every time.

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T cell differentiation and effector function regulation is an area that needs to be better understood. Until then, speculation combined with best practices help you determine your T cell population’s status. Functional profiling is the primary determinant with surface markers as supporting evidence to assess whether your T cell is activated, resting, or naïve. Here are ways to help differentiate your T cells.

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Poorly designed panels, failure to plan for the end-results, and not taking into account instrument characteristics, can all result in a failed sort, lost opportunity, and delay in the necessary data. Take the time at the beginning, before starting a rare event sorting, to understand the different issues that will potentially impact your outcome, and develop a plan to address each of them. Here are 4 considerations to keep in mind.

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Most of the interactions that a user has with a flow cytometer is with the fluidics system, and many of the issues that users will face in troubleshooting problems on the instrument will also be here. Understanding how the fluidics system works on your flow cytometer will help you prevent many common issues, prepare your samples correctly, and protect your data. Here are four important things to consider about the fluidics system in a flow cytometer.

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Fluorescence compensation is one of the more difficult, understandably confusing, and misunderstood aspects of flow cytometry. Understanding what compensation is, why it is necessary, and what to expect when using it, are critical for generating useful and high-quality data from flow cytometry experiments. The definition and mechanics of flow cytometry compensation and the critical concept that compensation’s most basic principle relies on are discussed here.

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Flow cytometry is a powerful technique impacting both clinical and research. When looking for a career, flow cytometry can take you many places. An experienced flow cytometrist can find a job in a biotechnology company, academia, a clinical setting, and more. To be successful in the field, it’s important to seek out new educational opportunities and network with your peers. Here are 5 tips that can help you turn flow cytometry into a successful career.

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While cells between 5-10 microns in diameter are typically the simplest cells to sort, quality must still be preserved to prevent sacrificing levels of purity, recovery, and viability. While sorting cells 5-10 microns in diameter does not present a particular challenge compared to other cell types, the standard procedures presented in this article must be followed to guarantee quality sorts, time and time again.

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There are numerous different ways to use keywords in FlowJo and other data analysis programs. The problem is most scientists fail to annotate their data properly and pay the price when they want to repeat their experiments. By taking advantage of the keywords listed in this article and by using keyword formulas, you can save time during your analysis. Most importantly, when you go to reanalyze your data, you can utilize your previous keywords and formulas to save even more time.

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Good flow cytometry depends on a high quality, single cell suspension. If the cells put through the instrument are not of high quality, the ensuing data will be difficult to analyze. Likewise, if the sample is clumpy, one will not be able to readily distinguish cells of interest from the clumps they are attached to. Sample preparation becomes the critical first step in any flow cytometry experiment. To get high quality results, follow these 3 sample preparation steps.

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This article is the second in a two-part series outlining some of the major components of the optical systems used in flow cytometry to provide insight and understanding to what happens before a signal is produced from the PMT detectors. Serving as a knowledge toolkit that can help troubleshoot problems you may encounter when performing your next cytometry experiment, this article investigates lenses, mirrors and filters in your flow cytometry equipment.

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Understanding the optical system of a flow cytometer may seem unnecessary for performing a typical experiment, but the more you know about your instrument, the better you will be at understanding your data, as well as troubleshooting potential issues. This article breaks down 4 elements of flow cytometer optics to provide a broad understanding on its impact on fluorescence.

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Every experiment has the goal of ensuring consistent and reproducible data. This makes the proper use of controls to establish the boundaries of gates critical. With the exception of one controversial control discussed in this article, each one of these gating controls plays an important and specific role toward the goal of reproducibility. Using these gating controls in every experiment will reduce data variability within the experiment, as well as between labs and institutions.

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Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today. Reproducibility continues to be a critical area that all researchers need to be aware of and researchers need to keep up on best practices to stay relevant. One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. This brief article explains why.

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The last 40 years have seen significant advancements in cell sorting technology. Cell sorting is often the entry point for many experiments. Fluorescent Activated Cell Sorting (FACS) combines the traditional power of flow cytometry and couples it with the ability to isolate the cells of interest. Understanding the inner workings of the instruments and some rules for preparing samples will lead to more successful experiments. Here are 4 essential facts about FACS.

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Multicolor sorting experiments can be complicated and if not setup properly, result in wasted time and suboptimal results. When setting up a multicolor experiment, the most saliently critical step is to set PMT voltages properly. In addition, using a viability dye and addressing doublet discrimination and setting the right sort regions and gates is important for any kind of flow cytometry experiment, but particularly for cell sorting. These tips help to ensure your setup is perfect to achieve results of the highest caliber.

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When sitting down to perform a new analysis of flow cytometry data, the researcher is guided by very particular laws of nature and a very specific method of working through a biological hypothesis to avoid shaping the results to his or her whims. Following these 5 data analysis and gating strategies through the hierarchy described in this article, researchers are provided with several strategies for identifying and displaying the most relevant data from their flow cytometry experiments.

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Implementing a system of quality assurance protocols lends confidence to the data collected, especially for those researchers performing longitudinal studies. Optimal instrument setting, cytometer sensitivity, and monitoring of day-to-day variability in measurement leads to improved assurance for those using this instrument to collect their critical data. QC programs will continue to be prudent measures for cytometrists to take as they align with the current emphasis on quality and reproducibility.

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When preparing figures for publication, the scientific question and hypothesis that forms the basis of the paper must be central and all the figures must be in support of that. The flow cytometry data that forms the basis of the conclusions should be presented clearly and concisely. While it provides pretty pictures and colorful layouts, the meat of the data are the numbers ― percentages of populations, fluorescent intensity levels and the like ― are what will convince the reader that the hypothesis tested is valid and well thought out. Here’s how to choose the correct flow figure for presenting your data.

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Cell sorters have become more sophisticated to rival the multicolor capabilities of analytical cytometers with cell sorting experiments becoming more complicated to match. While multicolor sorts are very feasible and can yield excellent results, success is always a product of very careful planning and optimization. From choosing optimal fluorophores to strategies to reducing spillover spreading, this comprehensive article is broken into two parts to give you all you need to play your experiments for advanced accuracy.

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Cellular proliferation is a critical component in biological systems. While normal cell proliferation keeps the body functioning, abnormal proliferation (such as in cancer) can be a target for therapy. There are several critical components in developing, validating and optimizing an assay to make these measures using flow cytometry. Knowing the steps to optimize these assays and properly interpret the results will help ensure the best data and best opportunities are pursued.

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Fluorochrome emission is the lifeblood of flow cytometry. The use of in silico tools, like spectral viewers, can save a lot of effort and missed opportunity by allowing for the modeling of excitation and emission profiles in the context of what filters a given instrument is equipped with. Using these tools, it is easy to identify where a new fluorochrome will be measured on an instrument, where a fluorochrome may cause issues with other fluorochromes, and what filters are best for detection. These tools can save a lot of troubleshooting at the beginning of an experiment, and also help provide understanding when issues appear.

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Sorting efficiency, in fundamental terms, is a real-time measurement, generated by the instrument, of how successfully its sorting system is able to resolve cells that we want to sort (target events) from cells we do not want to sort (non-target events). In order for the instrument’s sort output be acceptable with respect to the researcher’s needs, it is not sufficient to simply tell the instrument WHAT to sort, but is also critical to tell the instrument HOW to sort the target population. The HOW is determined by the sort modes.

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The most important part of executing a flow cytometry experiment correctly is actually understanding what you are doing. This means you must understand the terms and definitions that are critical to the field of flow cytometry. You must also be able to communicate your methodologies and results intelligently. To this end, we have compiled this list of the top 12 most commonly unknown or commonly misunderstood flow cytometry terms and definitions.

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Compensation in flow cytometry is a critical step to ensure accurate interpretation of data. It is also one of the areas that’s steeped in mystery, myths and misinformation. Manually adjusting the compensation values based on how the populations look, or so-called ‘Cowboy Compensation’, is not the correct way to determine proper compensation. The best practices for compensation involve following some very specific rules. Here are 4 steps to correctly compensating 4+ color flow cytometry experiments.

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When training new users on data analysis, there are several different best practices and gating strategies you should incorporate into your analysis. There are also several misconceptions you must understand. There are 3 gates that many researchers are not using but should be using when analyzing their flow cytometry data. These gates are critical for good data analysis. They will help remove many confounding events that may be clouding your analysis, especially where rare events are concerned.

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Forward scatter detectors collect light at small angles relative to the incident beam and can take advantage of the fact that cells preferentially scatter light in this “forward” direction. Forward scattered light is traditionally and often effectively measured with a photodiode, rather than the more sensitive photomultiplier used to measure fluorescence and side scatter. Scatter gets dim very quickly when particles have diameters below the wavelength of illuminating light, considering that scatter intensity decreases with a dependence on r6 of the particle. Here’s how small particles affect light scatter.

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The T-Test compares the differences between the means of two populations to determine if the null hypothesis should be rejected. At a minimum, to perform the T-Test, one needs the means and standard deviations of both populations, and the number of measurements. The researcher also needs to set the threshold value, also termed the α. Then, you will compare this threshold to the P-value. If the P-value is greater than the α, there is no significance in the data. However, if the P-value is less than the α, there is significance in the data. Here’s how to run a T-Test.

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Methods sections in scientific papers are often unable to capture all the critical data necessary to accurately reproduce the results in another lab. Here, information is provided on two specific ways in which flow cytometry researchers are effectively communicating flow cytometry data and metadata to the greater flow cytometry community to improve reproducibility and consistency. These two ways include first, the use of the MIFlowCyt standard and second, sharing data using the Flow Repository.

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Reproducibility is a critical component of the scientific process. One cannot publish data if the experiments cannot be replicated.

Unfortunately, as Begley and Ellis pointed out in a commentary in Nature that when Amgen attempted to reproduce 53 “landmark” papers in the area of cancer research, only 6 papers were “confirmed.” What does that mean to you, the flow cytometry researcher? To avoid publishing errors that reviews despise, it’s important to follow and promote the best practices in the field, thus ensuring that your data is reproducible to investigators attempting to validate your research. In particular, you must follow these 5 experimental tips.

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T regulatory cells (Tregs), formerly known as T suppressor cells, are a T cell subset with direct roles in both autoimmunity and responses to pathogens.

Tregs decrease inflammation via the secretion of immunosuppressive cytokines (IL-10, TGF-b) and also through direct suppression of inflammatory effector T cells (such as Th1 and Th17 cells). Given the importance of this unique T cell subset in so many immune responses, many investigators feel remiss if they immunophenotype their cell populations of interest without including a Treg measurement in the mix. But quantifying Tregs can be complicated. This article will show you how to quantify Tregs and how to ensure you’re measuring true suppressor T cells.

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The cell sorting process is inherently stressful.

Cells are first manipulated in suspension for up to several hours to prepare and stain them. Then, during the cell sorting process, these cells are pushed through narrow tubing under high pressure in the range of approximately 10-70 PSI, rapidly depressurized after passing through a nozzle, and then jetted through the air at velocities of 20 m/s (~44 MPH) or higher. Keeping cells healthy, happy, vital, and viable over the course of a cell sorting experiment is important to keep cells alive during the sort but also that the recovery of cells from the sort is high. Here are three things you can do to help ensure high levels of viability.

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The Complete Blood Count is a powerful addition to many flow cytometry workflows.

The CBC is an automated hematology test that looks at the levels of all the cells in your blood, providing your physician with valuable information about your health. Using just a small sample of blood, the CBC generates an extensive amount of information WITHOUT the need for centrifugation or multi-color staining experiments. Running a CBC is fast, easy, and inexpensive. In the world of clinical research, a CBC should always be run on the human clinical research samples. As a result, any obvious outliers can be removed from the study, reducing the spread of the data and reducing the risk of confounding your interpretation of the data. Here are the major advantages of obtaining a CBC by flow cytometry.

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When setting up a cell sorting experiment, there are many things to consider.

You must consider which controls you’re going to use, how you’re going to compensate the experiment, which instrument and which instrument settings are ideal, and how you plan to analyze, gate, and present your data. With so many things to consider, it’s easy to lose site of the small things that can drastically affect the viability of your cells, including the composition of your suspension buffer. The composition of the suspension buffer for preparation, staining, analyzing and sorting is perhaps the most important parameter for maintaining viability during a cell sorting experiment. While the precise components of a buffer can differ depending on the cell type, there are a 5 key points to keep in mind.

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There are several methods for analyzing live, dead, and apoptotic cells by flow cytometry.

As cells die, the membrane becomes permeable. This allows for antibodies to penetrate the cells, which can now mimic live cells. For this and other reasons, it’s important to remove dead cells from further analysis during your flow cytometry experiments. For example, let’s say you merely need to generate an accurate cell count. If you fail to remove your dead cells first, you might think you’re seeding 10,000 cells, but in reality only 7,000 of your cells are actually viable. Since the dead cells in your sample will not divide, your culture will take extra time to reach the needed level of confluence. Don’t make the mistake of forgetting to add a live-dead cell marker to your next flow cytometry experiment. Here are the top 3 markers available to you.

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8-peak beads, sometimes called “rainbow” beads, are a set of beads in a single vial that contains 8 different populations that differ only in the amount of fluorophore contained within them. One of the peaks, termed Peak 1, is unlabeled, and the additional seven, termed Peaks 2-8, contain increasing amount of fluorophore. 8-peak beads are designed to fluoresce in all channels on most flow cytometers and cell sorters. These beads are used to check fluorescence sensitivity and resolution by measuring the position of the unlabeled peak and the separation between all of the peaks, respectively. They are also used to check linearity in fluorescence detection channels by correlating the amount of fluorophore on each population of bead with the position on the scale onto which the flow cytometer places the beads. You may have noticed that when you use your 8-peak beads that your peaks have different CVs and intensities – some are wider and taller than others. But do you know why? If not, how do you know if your cytometer or cell sorter is performing correctly? Here’s everything you need to know about using your 8-peak beads.

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FMO controls are samples that contain all the antibodies you are testing in your experimental samples, minus one of them. When analyzing the minus, or left out parameter in an FMO control, you give yourself a strong negative control to work with. It’s a strong negative control because the left out marker in the FMO control allows you to take into account how the other stains in your panel affect the respective minus parameter. Many flow cytometry gates are difficult to define. This is especially true when you’re looking at activation markers within a continuum or accounting for the large data spread that occurs when compensating a 10+ color experiment. The only way to convince reviewers that your gate is in the proper place is by using FMO controls. Here’s why you need to use FMO controls for any multicolor flow cytometry experiment and how to prepare these controls properly.

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Electrostatic cell sorting is a complicated process that continues to be improved. It can be a struggle to understand exactly how all of the sorting components coalesce to accomplish the cell sorter’s tasks. For many scientists, the most difficult parts of the sorting equation are how droplets are charged and how drop delays are calculated. By understanding these two things, you will be in a better position to set up a successful fcell sorting experiment, which will help you achieve high sort recovery values, allowing for the accurate analysis of your cells and more cells to work with for your downstream experiments. Here’s how cell sorting droplets are charged and how cell sorting drop delays are determined.

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Flow Cytometry is a remarkably powerful tool for the study of T cells. It has been successfully used for many decades to accurately visualize and enumerate a variety of T cell subsets. With a large sensitivity range for fluorescent probes, >95% sampling efficiency, and the ability to sort populations of interest for further study, fluorescent-based cytometry remains a tool of choice for T cell analysis. The key is to define your T cell populations of interest with correct gating strategies and to back up your T cell subset findings with functional analysis of these subsets. A cell’s actions should guide its definition, not the other way around.

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The International Cytometry Certification Exam was developed over a period of several years. The goal was to ensure a base level of flow cytometry knowledge in certificate holders. Some cytometrists have deemed the ICCE as unnecessary. Others have voiced concerns about the specialization of certain exam subsections. However, despite these concerns, the ICCE is here to stay. The exam and certification process as a whole has the support of multiple companies that are providing training, as well as the support of ISAC, ICCS, and the Wallace H. Coulter Foundation. In the end, the most telling test of the value of the ICCE will be when flow cyotmetry job postings in basic research start asking for the certification. This day may not be too far away.

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Microvesicles originate from cells and have the same analysis requirements as cells. For these and other reasons, flow cytometry is a popular choice for microvesicle analysis. However, there are pitfalls with small particle flow cytometry that have led to many conflicting publications. The only way to avoid these mistakes is to first identify them and then take measures to prevent them. The following are 4 common mistakes researchers make when preparing microvesicle flow cytometry experiments, as well as how to prevent these mistakes.

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All the experiments and experience in the world do not count if you are unable to communicate your results to the scientific community. As part of that communication process, your paper will undergo the dreaded ‘Peer-Review’ process. If you wish your paper to survive this process, you must collect, analyze, and present your flow cytometry data properly—before you submit your paper. A review of the following questions, as well as how to answer them, will help ensure your paper is not rejected. Here are 5 specific questions reviewers will ask when reviewing your flow cytometry data.

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To make certain your instrument is set up correctly for your experiments, manufacturers have developed defined polystyrene beads.

These beads’ consistent nature helps you to assess how your instrument is behaving, helps you set up proper compensation matrices, and helps you generate volumetric counts of your cell populations. Alignment, sensitivity, and fluidic quality control beads will help you to ensure that with the same wattage on the laser and the same voltage applied to the detector returns the same median fluorescence. The right compensation capture beads will bind antibodies of multiple isotypes from multiple species and give you a very bright positive signal from which you can calculate a correct compensation matrix. The use of counting beads allows you to easily calculate your cell concentration in your sample. Together, these beads will make your life easier and help you get your data published. Here are the 3 beads you should use.

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In today’s world, many scientists have access to instruments capable of running experiments with 10 or more colors.

The leap from 2 to 10 colors may seem small, but here are many factors to consider in the design and analysis of experiments that makes full use of instruments that can handle these additional colors. Imagine analyzing a 2-color experiment. With 2 biaxial plots and a single quadrant gate, you have only 4 populations to report. Now add a 3rd color. By doing so, you’ve increased your population count to 8. With 4-colors, you’ve increased your population count to 16. On and on it goes until you get to 10-colors. Now you have 1024 possible combinations! With this kind of complexity, careful experimental planning is not a luxury, it’s a necessity. Here are 7 tips for preparing and analyzing 10-color flow cytometry experiments.

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Flow cytometrists use the Jablonski diagram to aid in understanding and explaining the kinetic events of fluorescence.

Fluorescent compounds start at the ground state until they are excited by interacting with a photon of light. This photon excites the compound, promoting an electon to a higher energy state. Some of this energy is lost by emission of heat and other non-radiative processes, leading to the previous energy state. Finally, an electron falls back to the ground state while releasing a photon of light. This photon has a lower energy (higher wavelength) than the exciting photon of light. Here’s how understanding this process can help you get published.

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The field of flow cytometry is moving beyond the use of isotype controls, with many suggesting they be left out of nearly all experiments.

Yet, isotype controls were once considered the only negative controls you should ever use. They are still very often included by some labs, almost abandoned by others, and a subject of confusion for many beginners. What are they, why and when do I need them? Are they of any use at all, or just a waste of money? Most importantly, why do reviewers keep asking for them when they review papers containing flow data? Here is everything you need to know about using (or not using) isotype controls in your next flow cytometry experiment.

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With the increased development of fluorescently conjugated monoclonal antibodies came more applications with potential clinical impact.

In bone marrow transplantation, studies using hematopoietic cytokines made it feasible to gather stem cells from peripheral blood. It was also shown that reconstitution of bone marrow was accelerated when using cell from peripheral blood rather than bone marrow. Many more clinical flow cytoemtry applications have been developed. All of which should follow these 6 keys of running clinical flow cytometry experiments.

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Written by Tim Bushnell, PhD Flow cytometry data analysis is getting more complex. Gone is the rule of 2-3 color experiments. Even beginners are starting with 5+ color assays, and the adoption of mass cytometry has the potential to increase our headaches even more. Current data analysis methods are good for single tubes or small cohort…

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Written by Tim Bushnell, PhD What happens if one combines the power and speed of traditional flow cytometers with the resolution of a microscope? Cytometry is the study of biological processes at the whole cell level and includes techniques like light microscopy and electron microscopy. But microscopy by itself is a bit different. From the earliest…

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Manual compensation is the process of adjusting the compensation based on how the data visually looks.

If you have manually compensated data in your lab notebook–strike it out now. Manual compensation results in overcompensated data, yielding incorrect conclusions. If you have issues, explore what those problems are and work to resolve them rather than making up fiction by manual compensation. Here are three keys to automatically compensating your data.

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Written by Tim Bushnell, PhD We all know that flow cytometry makes individual measurements on large populations of cells, it allows for statistical analysis of the data, lending strength to a researcher’s conclusions. Likewise, the isolation of very complex populations by flow cytometry cell sorting can help lead to a richer understanding of the intricate…

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Written by Tim Bushnell, PhD Pairing highly expressed antigens (like CD3) with dimmer fluorochromes, and the antigens of interest with the brightest fluorochromes, is a key part of panel design with few tools to help. With early generation instruments, this was relatively easy to determine, since fluorochrome choice was limited. With the advent of instruments…

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Written by Tim Bushnell, PhD BD Biosciences brand of digital flow cytometers, including the FACSCanto, the LSR-II, FACSAria and Fortessa, utilize a software acquisition program known as FACSDiva. Diva is aptly named as it can be a difficult program to master. However, Diva has come along way over the past 10 years and many improvements…

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Written by Tim Bushnell, PhD Cell death is a fact of biological life.  How, when, where and most importantly, why cells die, can have huge biological consequences on the path an organism may take. Apoptosis, or programed cell death, can result in a selective advantage for an organism. Fingers, for example, are the result of apoptosis…

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Written by Tim Bushnell, PhD I often have researchers come into the core wanting to look at the activation and downstream signaling events that occur in different immune cells. These events occur in response to signals such as cytokines, chemokines, various receptor ligands, and the engagement of the T cell or B cell receptors. The…

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Written by Tim Bushnell, PhD I’ve been in the world of flow cytometry and cell sorting for a very long time. Now, don’t worry, this isn’t going to be some lament about the “good old days.” Well, maybe just a little. But there will be helpful takeaways, I promise. I was trained by the incredible…

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Written by Tim Bushnell, PhD What is the top flow cytometer? The easy answer is the flow cytometer that matches your needs and fits within your budget. However, before running off to spend cash, consider the following. What are the current needs of the users? Evaluating the user’s needs will help define the parameters needed…

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Written by Tim Bushnell, PhD As with all complex technology there are many different levels of education that users should avail themselves of. Basic instrument operation This level of education is akin to learning how to drive a car.  At this level, the focus will be on how to put the sample on the instrument,…

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Written by Tim Bushnell, PhD Cell proliferation can readily be measured by flow cytometry.  Depending on the research question, there are several different techniques that can be used. Cell counting experiments This relatively straightforward experiment where the investigator uses one of several counting techniques to see if there is an increase in the populations. This…

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Written by Tim Bushnell, PhD Flow cytometry is a powerful tool for asking and answering questions at the whole cell level. The first step in any flow cytometry experiment is to define the hypothesis or biological question that is to be answered.  This helps ensure that flow is the correct technique for answering the question.…

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Written by Tim Bushnell, PhD Flow cytometry can be an intimidating tool for the new cytometrist.  There are many sources that one can turn to for training and education. Local Shared Resource Lab Manager If you institute has a shared resource lab (a ‘core facility’), look in what training they provide.  This is your first…

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Written by Tim Bushnell, PhD Cell sorting remains the best tool to isolate and purify cellular populations that can be phenotypically defined. This is especially true for rare-event detection and purification. Successful rare event detection and purification requires some attention to ensure the best yield and purity. 1) Watch the instrument to ensure success a)…

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Written by Tim Bushnell, PhD Mass Cytometry, commercialized by the company DVS Sciences, in the instrument called the CyTOF is a newly emerging technology in the field of flow cytometry.  This technology replaces traditional fluorescent-labeled antibodies with highly purified, stable isotopes with very well characterized mass values.  This extends the power of flow cytometry from…

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  Written by Tim Bushnell, PhD Cell sorting can be a scary proposition. A precious sample is introduced into a machine that pressurizes the cells to 70 PSI, moves them past one or more lasers, vibrates the stream at 90 kHz before decelerating the cells to atmospheric pressure before they hit an aqueous surface. Many…

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Written by Tim Bushnell, PhD Cell Sorting is the process of isolating cells after the identification of the cells using the principles of flow cytometry. The upstream components of the cell sorter are common to all flow cytometers. The difference comes in what is done with the cells after they have been interrogated and identified.…

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A variation on biexponential scaling similar to logicle scaling. The biexonential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone. Biexponential scales are more generally referred to as hybrid scales and include other variations like lin/log or log with negative. More information on Hyperlog…

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Written by Tim Bushnell, PhD Cell cycle analysis by flow cytometry uses a DNA binding dye, such as propidium iodide (PI), 7- aminoactinomycin D (7-AAD) or 4’,6-diamidino-2phenylindole (DAPI), to determine the cell cycle state of a cell population. The Gap1 (G1) phase of an eukaryotic cell is defined as having 2C DNA. The synthesis (or…

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Written by Tim Bushnell, PhD The question always arises as to what is the top cell sorter on the market. This question is a difficult one to generalize because there are several considerations that need to be made in choosing a cell sorter. What are the sorting needs of the investigators? If all the investigators…

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Written by Tim Bushnell, PhD DNA cell cycle analysis is a very powerful technique in flow cytometry. It is deceptively easy, but there are several critical things to remember to ensure successful analysis. Collect enough events. Cell cycle analysis involves fitting of the data using one of several mathematical models that describe the behavior of…

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Written by Tim Bushnell, PhD With the ability to capture expression data at the single cell level through many thousands of cells in a short time, flow cytometry data is very numbers rich. The importance of those numbers and how to use them in hypothesis testing is critical to ensure the robustness of the analysis.…

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An implementation of biexponential scaling published by the Herzenberg lab at Stanford. The biexonential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone. The “logicle” implementation of biexponential was implemented in many popular software packages like FACSDiva and FlowJo. Other types of biexponential scaling…

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Written by Tim Bushnell, PhD After completing the perfect staining and cytometry run, the hard work begins – data analysis.  To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate…

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Written by Tim Bushnell, PhD Depending on the experimental design, many researchers will be doing complex assays that will require statistical analysis to determine if the hypothesis being tested is statistically significant or not. Unfortunately, many researchers go about this analysis the wrong way, resulting in spurious conclusions. The following points are guides to help…

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Written by Tim Bushnell, PhD A laser type in a flow cytometer with a wavelength of about 560nm. The green and yellow laser are more effective at exciting PE and its tandems than the traditional blue laser. The yellow laser is also often used to excite the “fruit” dyes like mCherry. For more information, please…

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Written by Tim Bushnell, PhD After completing the perfect staining and cytometry run, the hard work begins – data analysis. To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate…

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Written by Tim Bushnell, PhD The laser type in flow cytometers with a wavelength of around 530nm. Standard “green” lasers are about 532nm, but vary between 530nm and 535nm usually. The green and yellow laser are more effective at exciting PE and its tandems than the traditional blue laser.

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A laser with a wavelength in the UV range. Typically in flow cytometers, the UV laser has a wavelength of 350nm or 355nm. Some have a wavelength of 375nm.

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Another very common laser after the “blue” and “red” laser in flow cytometers. A “violet” laser in flow cytometry typically is referred to as the 405 because most flow cytometers use a violet laser with a wavelength of 405nm. Pacific Blue and Pacific Orange are the most common fluorophores used with this laser, but Brilliant…

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The second most common laser in a flow cytometer after the “blue” laser. The “red” laser typically has a wavelength of 633nm, but new flow cytometers are starting to use a “red” laser with a wavelength of 640nm. The most common fluorophores excited and detected off this laser are APC, Alexa Fluor 660, Alexa Fluor…

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The most common laser type in a flow cytometer. Typically, this laser has a wavelength of 488nm in flow cytometers.  In fact, the term “Blue” laser is often interchanged with “488” laser. Frequently used fluorophores excited and detected by this laser are FITC, Alexa Fluor 488, PE, PerCP, and their tandems.

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A filter that allows light between a set wavelength to pass through and reflects light above and below the set wavelength. For example, a bandpass filter with a wavelength of 550/40nm would allow light between 530nm and 570nm to pass through, but reflect light below 530nm and above 570nm.

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A filter that allows light over a set wavelength to pass through and reflects light above the set wavelength. For example, a shortpass filter with a wavelength of 450nm would allow light with a wavelength less than 450nm to pass through the filter, but reflect light higher than 450nm.

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A filter that allows light over a set wavelength to pass through and reflects light below the set wavelength. For example, a longpass filter with a wavelength of 670nm would allow light with a wavelength greater than 670nm to pass through the filter, but reflect light lower than 670nm.

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Counted by the Photomultiplier Tube (PMT) in the flow cytometer. Photons enter the PMT and the signal is amplified in the PMT when a photon strikes the anode and “knocks” of electrons. These electrons then hit a series of subsequent anodes, amplifying the total number of electrons of signal. The PMT then counts the total…

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A type of flow cytometer manufactured and sold by BD Biosciences. This instrument was one of the first mass produced flow cytometers. The FACSCalibur is still prevalent in many labs around the world. While only a four color, six parameter analog system, this machine is stable and rarely requires service. It has gained a reputation…

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Written by Tim Bushnell, PhD Flow cytometry is a complex technology that requires understanding of sample processing, data acquisition and data analysis.  An individual experiment can take a dozen hours to prepare, hours to collect and days to analysis.  This is why flow cytometry training is critical in understanding and optimizing the use of this…

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Written by Tim Bushnell, PhD If you’d like a job in a flow cytometry core or lab, ExCyte can give you all of the training you need. Combined, our instructors have over 100 years of flow cytometry experience. If you are interested in researching available flow cytometry jobs, there are number of online resources that…

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Written by Tim Bushnell, PhD Individuals can now be certified in flow cytometry by taking the International Cytometry Certification Exam, which is jointly managed by the International Society for the Advancement of Cytometry (ISAC) and the International Clinical Cytometry Society (ICCS).  This exam covers the general principles of cytometry in a multiple-choice format.  Individuals who…

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Written by Tim Bushnell, PhD Understanding statistics and fow cytometry statistical analysis is critical to understanding flow cytometry data. One of the powers of flow cytometry is the fact that we generate large amounts of data that are amenable to statistical analysis of our populations of interest.  Using the standard set of statistical analysis tools…

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Written by Tim Bushnell, PhD Flow cytometry is the science of measure the physical and biochemical processes on cells and cell-like particles. This analysis is performed in an instrument called the flow cytometer.  FACS Analysis is the short-hand expression for this type of cell analysis The term FACS stands for Fluorescent Activated Cell Sorting, a…

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Written by Tim Bushnell, PhD Every fluorophore has a unique excitation and emission profile which is usually displayed on a spectral viewer, or spectral graph. The combination of the excitation and emission profiles is the fluorophore’s spectral profile. Every fluorophore has a peak excitation wavelength (the wavelength at optimal excitation) and a peak emission wavelength…

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Written by Tim Bushnell, PhD PBS is the acronym for phosphate buffered saline. Phosphate buffer is one of the most common buffers used in biological research.  The phosphate serves as a buffer to keep the pH constant, while the saline is referencing the osmolarity.  Additional ions such as Ca2+ or Mg2+ , energy sources like…

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Written by Tim Bushnell, PhD What is autofluorescence? Autofluorescence is the term given to describe the natural fluorescence that occurs in cells. The common compounds that give rise to this fluorescence signal include cyclic ring compounds like NAD(P)H, Collagen, and Riboflavin, as well as aromatic amino acids including tyrosine, tryptophan, phenylalanine. These compounds absorb in…

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Written by Tim Bushnell, PhD Dynamic range is the total range of fluorescent values obtained from a particular flow cytometry assay. It is defined as the ratio of the largest possible fluorescent signal to the smallest possible fluorescent signal. The dynamic range can vary based on the application. For example, a cell cycle assay may…

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Written by Tim Bushnell, PhD The Fluorescence Minus One Control, or FMO control is a type of control used to properly interpret flow cytometry data.  It is used to identify and gate cells in the context of data spread due to the multiple fluorochromes in a given panel. An FMO control contains all the flurochromes…

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Written by Tim Bushnell, PhD Sheath fluid is the solution that runs in a flow cytometer.  Once the sheath fluid is running at laminar flow, the cells are injected into the center of the stream, at a slightly higher pressure.  The principles of hydrodynamic focusing cause the cells to align, single file in the direction…

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Written by Tim Bushnell, PhD In flow cytometry, cells, in suspension are moved from the tube to the interrogation point and finally into the waste (or to be sorted, but that is a different story).  To do this, the fluidics components of the flow cytometry are required. The fluidics are comprised of a running (or…

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Written by Tim Bushnell, PhD Do you know what an isotype control is? Isotype refers to the genetic variation in the heavy and light chains that make up the whole antibody moiety. In mammals, there are 9 possible heavy chain isotypes and two light chain isotypes. Every antibody will have a specific isotype, and this…

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Written by Tim Bushnell, PhD Titration is the process of identifying the best concentration to use an antibody for a given assay. While the vendor will provide a specific concentration to use, this may not be appropriate for your assay. Performing titration is a simple process: fix the cell concentration, the time of incubation, the…

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Written by Tim Bushnell, PhD Differential pressure based flow cytometers currently dominate the market. These systems have two pressure regulators. The first is at a constant pressure that sets how fast the fluids runs at. The second is regulated by the investigator (like as shown on this LSR-II control panel). As the sample pressure goes…

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Written by Tim Bushnell, PhD I’m a scientist, not Perry Mason. I’m not a defense attorney or a detective. Why would I say that? Because well before March grant writers need to be a Perry Mason – make the case and defend it to have a successful Shared Instrument Grant (SIG). For those running core…

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Written by Tim Bushnell, PhD ExCyte chooses to train people in flow cytometry because we know what it’s like to feel the pain of ruined experiments. No one enjoys wasting thousands of dollars on reagents and priceless amounts of instrument and personnel time. This is especially true when grant funding comes into play. No one…

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Written by Tim Bushnell, PhD Flow cytometry education has grown phenomenally in response to more sophisticated instrumentation, growing demands for more sensitive, high-speed and multi-parameter flow. Specialized training is critical to any flow lab competing in today’s global marketplace. The key is to find the right trainer. As a core director or lab manager, how…

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Written by Tim Bushnell, PhD A Jablonski diagram illustrates the electronic states of a molecule as well as the transitions between them. These states are arranged vertically by energy and grouped horizontally by spin multiplicity. Nonradiative transitions are indicated by straight arrows and radiative transitions by squiggly arrows. The vibrational states of each electronic state…

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